The quantification of the specific disease-associated populations of circulating extracellular membrane nanovesicles (ENVs) has opened up new opportunities for liquid biopsy in cancer and other chronic diseases. However, the sensitivity of such methods is mediated by an optimal combination of the isolation and labeling approaches, and is not yet sufficient for routine clinical application. The presented study aimed to develop, characterize, and explore a new approach to non-specific ENV staining, followed by size-exclusive chromatography (SEC), which allows us to increase the sensitivity of bead-assisted flow cytometry. Plasma from healthy donors was purified from large components, stained with lipophilic CM-Dil dye, and fractionated by means of SEC. The obtained fractions were analyzed in terms of particle size and concentration using NTA, as well as vesicular markers and plasma protein content via dot-blotting. We characterized the process of CM-Dil-stained plasma fractionation in detail and indicated the fractions with optimal characteristics. Finally, we explored the sensitivity of on-bead flow cytometry for the analysis of specific populations of plasma ENVs and demonstrated the advantages and limitations of the proposed technique. Plasma-circulating membrane extracellular nanovesicles (ENVs) have great diagnostic potency, especially in oncology . The devThe first issue is the selection of an optimal approach to flow cytometry (FCM). Currently, two methods are available: conventional FCM, wherein an instrument detects artificial beads with ENVs attached to the surface, and so-called high-resolution FCM (hrFCM), wherein an instrument is used for the detection of individual ENVs. The opportunity for the multi-parametric analysis of individual particles is an obvious advantage of hrFCM. However, this approach involves high requirements with regard to the sensitivity of the device and the skill of the performer. For instance, recently published guidelines  suggest the reporting of 25 experimental components for the confirmation that the correct experimental conditions have been reached . The altAnother concern is the method of fluorescent labeling of on-bead ENVs. In the above-cited reports, ENVs were labeled with antibodies specific to markers of interest. However, this approach assumes double-selection of the vesicles to be quantified: first by immune-capturing, and then by immune-labeling. This technique can be useful for experimental research, but can produce a biased clinical interpretation. Moreover, antibodies might have a relatively high cost, low stability, and batch-to-batch variability. Therefore, the use of only one type of marker-specific antibody (either for capturing or for detection) would decrease the cost and increase confidence in the results. According to this consideration, combining immune-capturing and the non-specific labeling of vesicular membranes might present an optimal strategy. Non-specific labeling of ENVs with lipophilic dyes provides many advantages, including a relatively low cost and high efficacy. However, the common disadvantages, such as micelle formation ,15 and tConsidering the general goals of clinical applicability and argumentation, as described above, we utilized a pre-analytic workflow, including the labeling of plasma with a lipophilic dye, followed by SEC . Next, tg—10 min, 800× g—10 min, 1500× g—10 min, and 17,000× g—30 min. Each time, the supernatant was carefully replaced in a new tube. After the last centrifugation, the supernatant was filtered through a 0.2 mkm PES syringe filter to obtain pellet pure plasma (PPP). Plasma treated in this way was used in all experiments described in this study.Plasma was obtained from healthy donors after they signed a voluntary and informed study participation form. Blood was collected in BD Vacutainer spray-coated EDTA tubes; plasma was immediately separated from the blood, aliquoted, and stored at −80 °C. Before use, plasma was slowly thawed at 4 °C. In order to remove cells and cellular detritus, plasma was centrifuged at 400× g for 2 h, the supernatant was removed, and the precipitate was dissolved in PBS. The solution was then centrifuged again at 110,000× g for 2 h. The supernatant was then removed, and the pellet was resuspended in 500 μL of PBS. To isolate the pure cancer cell-derived ENV population, a colon cancer cell culture (Colo320) was cultured under standard conditions in RPMI medium with 10% FCS and penicillin–streptomycin until confluence was reached at 50%–60%. Then, the medium was replaced by fresh serum-free medium and cells were cultured for 2–3 days until they reached 90%–100% of the confluence layer. The medium was collected; centrifuged at 800× g—15 min, 1500× g—15 min, and 17,000× g—1.5 h; and then filtered through a 0.22 mkm PES filtration system. ENVs from the 180 mL pre-treated medium were sedimented by means of ultracentrifugation at 110,000g for 4 h, the supernatant was removed, and the precipitate was dissolved in PBS. The solution was centrifuged again at 110,000× g for 2 h. The supernatant was removed, and the pellet was dissolved in 500 μL of PBS.PPP (2 mL) was mixed with PBS in a 1:1 ratio. ENVs were sedimented by means of ultracentrifugation (UC) according to the classical procedure, with minor modifications, using an Optima XPN 80 ultracentrifuge (rotor 70.1 Ti/k-factor 36). Briefly, the 50% plasma solution was centrifuged at 110,000× 12 particles/mL were deposited on a carbon-coated copper mesh/Lacey Carbon Supported Copper Grids, 50 nm in size . The excess sample was removed with filter paper. After that, the sample was immersed in liquid ethane for rapid freezing and transferred to a cryostat for subsequent analysis using a cryo-microscope.The morphology analysis of ENVs isolated from plasma via UC was performed with a Jeol JEM-2100 microscope at the Research Resource Center for Molecular and Cell Technologies of Saint-Petersburg State University. The samples of the ENVs at a concentration of 10An amount of 1 mg/mL (1 µM) of a stock solution of CellTracker CM-Dil reagent  in 100% DMSO was prepared via ultrasonification  and stirring until the dye was completely dissolved. The stock solution was diluted 1:20 and 1:400 to obtain 50 nM and 2.5 nM solutions in DMSO. Then, 2 mkl of the prepared CM-Dil solutions were added to 2 mL PPP, and incubated for 20 min at 37 °C with moderate stirring. Thereafter, the samples were immediately uploaded onto prepared (washed with PBS) SEC columns . Twenty-three fractions of 500 μL were collected and analyzed.The measurements were performed using the Nanosight NS300 analyzer . In order to analyze ENVs isolated via ultracentrifugation, samples were diluted in ratios of 1:100 and 1:1000, and each dilution was studied in 4–5 different micro volumes by pumping the sample through a chamber. The duration of each measurement was 30 s. Camera level: 14. Shutter slider: 1259. Slider gain: 366. Repeated results, obtained with the optimal dilution, were used after averaging.In order to measure the size and the concentration of particles distributed by SEC, the following fractions of plasma (PL) and CM Dil stained plasma (PL-Dil) were assayed: combined 5 and 6 fractions, 7, 8, 9, 10, 11, 12, 13, 14, 15, and combined 16–21. The SEC fractions of CM Dil solution were combined in five pools: 5–6, 7–8, 9–11, 12–15, and 16–21. Fractions 1–4 and 22–23 were excluded from the analysis as they were non-informative. All samples were diluted in the ratio of 1:20 by means of PBS before measurement. Camera level: 12–15 for pellet pure plasma and CM Dil-stained plasma, 15–16 for CM Dil solution. Slider shutter: 1200–1300. Slider gain: 175–295. Number of frames: 749. Detection threshold: 17 for plasma and CM Dil-stained plasma; 7 for CM Dil solution. Each measurement was carried out 6 times for 30 s and the results were averaged. The spectra were processed using Nanosight NTA 3.2 software. The results were transferred in an Excel table. Similar results obtained using NTA of plasma (PL) and CM Dil-stained plasma (PL-Dil) were combined, averaging them into pools of 7–8, 9–11, and 12–15 to be presented graphically. The raw data are available in the × 5 min), incubated at 4 °C for 30 min with secondary HRP-conjugated Fc fragment-free antibodies , diluted at a ratio of 1:25,000 in 0.1% BSA in TBS-T, and washed three more times with TBS-T (15 min × 2 times), then once with TBS (5 min). The membrane was soaked in Immobilon Classico Western HRP substrate  in the dark for 5 min. The images were obtained using the Invitrogen iBright FL1500 Imaging System.The 0.3-µL samples obtained via SEC ware spotted onto the nitrocellulose membrane. The fluorescence intensity of the CM-Dil dye was measured on an Invitrogen iBright FL1500 Imaging System with an exposure time of 0.016 s. The values were digitized using the ImageJ program. To assay the protein content of SEC-separated plasma fractions, the membrane with spotted samples was dried, blocked by soaking in 5% BSA in TBS-T in RT for 30 min, and incubated at 4 °C overnight with primary antibodies against CD9 , CD63 , HSP70 , HSA , Reg IV , and DHRS11 . Primary antibodies were dissolved to 1 mkg/mL in 0.1% BSA in TBS-T. Then, the membrane was washed three times with TBS-T (3 The pellet pure plasma (2 mL) was stained with CM-Dil (2.5 pM) and SEC fractionated. For the quantification of colon cancer-derived ENVs, plasma was mixed with a corresponding amount of COLO320-derived vesicles before being stained with dye. SEC fractions 9, 10, and 11 were pooled and 100 µL of each pooled sample was incubated with 0.07 µg of the biotinylated antibodies—HSP70 , DHRS11 , and Reg IV —overnight at 4 °C with gentle stirring. Superparamagnetic beads (SPMB) coated with streptavidin  were washed three times with PBS and blocked with 0.2% Protein-Based Blocking Reagent  for one hour at 4 °C with gentle stirring. Pre-treated SPMB (1 µg) were gently mixed with ENVs coupled with biotinylated antibodies for one hour at 4 °C. The obtained complexes were washed three times to rid them of excessive ENVs. In order to label CD63 markers on the surface of on-bead-attached ENVs, complexes were additionally incubated with FITC-conjugated, mouse monoclonal MEM-259 against CD63  and washed again. Finally, the SPMB-ENVs complexes were resuspended in 200 µL of PBS and analyzed using CytoFLEX. Data processing was performed with the FlowJo™ v10.7 software.A goal of the study was to estimate whether CM-Dil plasma staining followed by SEC presents an efficient approach for on-bead FCM of plasma ENVs. With this purpose, three objectives were addressed .First, we aimed to evaluate the efficacy of plasma component labeling with CM-Dil. Two samples—pellet pure plasma (PPP) and plasma ENVs isolated by ultracentrifugation (ENVs)—were included in this experiment. SEC fractions from 5 to 23 were analyzed. Next, we planned to characterize particles that may be presented in CM-Dil-labeled plasma; CM-Dil stained PPP was compared with PPP and CM-Dil solution in PBS. SEC fractions from 5 to 21 were analyzed. Finally, we planned to evaluate the efficacy and sensitivity of on-bead FCM of CM-Dil-stained and SEC-purified plasma ENVs in different experimental settings. Fractions 9, 10, and 11 enriched by ENVs were included in analysis. The corresponding methods and samples used are indicated in 12 particles/mL. Vesicles were visualized using Cryo-TEM  via two rounds of ultracentrifugation, as described. The concentration of vesicles was measured via NTA and adjusted to 10Cryo-TEM . The cluThe protocol we used as a prototype  included9/mL) of particles, at around 100 nm in size. These results reflect the predictable presence of ENVs and are consistent with the fluo-dot-blot results (10/mL) than the amount of ENVs (see the graph for details). We can suppose that these fractions included plasma lipoproteins and protein complexes big enough to be detected by NTA. The CM-Dil-stained plasma appeared to be quite similar to plasma  decreased, whereas the number of lighter particles (fractions 12–15) increased due to CM-Dil staining. This shift might reflect the change in particle density due to the incorporation of CM-Dil. Moreover, in both samples (plasma and CM-Dil-stained plasma), fractions 9–11 included a similar number of 100-nm particles, which were mostly represented by plasma ENVs (as shown in 10 (as shown in the graph). As the difference in these fractions between the analyzed samples (plasma VS. CM-Dil-stained plasma) was mediated by the presence of the dye only, a twofold increase in the light particle number in SEC fractions 16-21 might be the result of the formation of new complexes by CM-Dil and small plasma components. The results of the pure CM-Dil fractionation are presented in 8–8 × 108/mL) of particles, with a size of about 60–70 nm. These particles may simply have been micelles formed by CM-Dil, as they had a rather different density. The presence of heavy particles in SEC fractions 5 and 6, as well as the absence of light particles in SEC fractions 16–21, were the main differences between the suspension of CM-Dil-formed micelles and the plasma samples. It can be noted that the heavy particles fractioned in fractions 5 and 6 can be formed by CM-Dil in the absence of plasma only; the presence of plasma somehow prevents the formation of such particles. Moreover, an analysis of the CM-Dil suspension confirmed our previous conclusion regarding the relatively light and small components of plasma observed in fractions 16–21; these particles existed in plasma samples only. This experiment allowed us to compare the particle size/density distribution in plasma, CM-Dil-stained plasma, and the CM-Dil suspension and to identify the SEC fractions enriched by ENV-sized particles and minimally contaminated by CM-Dil-formed micelles.As indicated in a number of reports, lipophilic dyes such as CM-Dil may form micelles that are similar in size and structure to ENVs. As these micelles may interfere with the further analysis of CM-Dil-stained plasma ENVs, we intended to track their formation. The aim of our next experiment was to evaluate the size and concentration of components of SEC fractions obtained using separation plasma (PL), CM-Dil-stained plasma (PL-Dil), and CM-Dil alone. The latter two samples contained the same amount of dye (2.5 pM). After performing SEC, fractions 5 and 6, 7 and 8, 9–11, 12–15, and 16–21 were combined and measured by means of NTA. The results are presented in  results A. The neTaken together, the results of the NTA reveal that the number/density of plasma particles of vesicular size (100 nm) was less influenced by staining with CM-Dil in fractions 9–11: these fractions, in CM-Dil-stained plasma, most likely include ENVs that are suitable for further analysis. In the next fractions, the size of particles became smaller in both plasma and CM-Dil-stained plasma samples, whereas staining with CM-Dil increased the particle number. These fractions in the CM-Dil-stained plasma most likely included particles of a non-vesicular nature and their analysis requires further attention. Fractions 5 and 6 and 16–21 should be excluded from further analysis because they may contain CM-Dil-formed micelles (in 5 and 6) and small plasma components (in 16–21).As is well known, different vesicular markers may be expressed in different vesicular populations. Apparently, these populations may be separated by means of SEC. We used dot-blotting for the relative quantification of vesicular markers CD63 and CD9, as well as abundant plasma proteins HSP70 and albumin in different SEC fractions of plasma. To explore the possible influence of CM-Dil staining, plasma and CM-Dil-stained plasma were assayed in parallel. The results of the CD63 analysis are presented in The results of dot-blotting also revealed an interesting effect of CM-Dil staining. Certain shifts in the blot signal intensity of later fractions were observed through a comparison of the plasma and CM-Dil-stained plasma samples. This may happen due to the incorporation of lipophilic dye into lipid-containing plasma components, resulting in a change in their density.By combining the results of the NTA and the dot-blotting, we can conclude that fractions 9–11, obtained by means of SEC of the CM-Dil-stained plasma, contained vesicles of 100 nm, with low CD63/CD9 representation, whereas fractions 12–16 contained vesicles of a smaller size; however, they were also CD63/CD9-positive.Next, we intended to evaluate the efficacy of the developed method of ENV labeling as a pre-analytic step in bead-assisted FCM. Super-paramagnetic beads were decorated with antibodies against HSP70 via a biotin–streptavidin link. Pellet pure plasma was stained with CM-Dil and SEC fractionated. Fractions 9–11 were combined and incubated with SPMB. Afterwards, SPMB with coupled HSP(+) ENVs were stained with FITC-labeled antibodies against CD63. Flow cytometry was performed to assay the signal intensity from both the antibodies  and CM-Dil . The scheme of the experiment and the results are shown in As fluorescent signals in two different channels were obtained from the same sample, the observed difference in the fluorescence intensity could only be caused by the different efficacies of ENV labeling. Thus, the non-specific labeling of the vesicular membrane with lipophilic CM-Dil dye turned out to be two times more efficient compared to the conventional method of common vesicular marker labeling with FITC-coupled antibodies.Considering potential clinical uses, the quantification of low-representative disease-associated ENV populations would be the most important application of the developed method. As the efficacy of CM-Dil labeling is relatively high, it is expected to mediate the high sensitivity of bead-assisted FCM. The final experiment aimed to prove this assumption using model mixtures of ENVs.9, 1010, 1011, and 2 × 1011) was mixed with pellet pure plasma (2 mL). The mixtures were CM-Dil-stained and SEC fractionated. Fractions 9–11 were incubated with SPMB decorated with antibodies against proteins of interest. As in previous experiments, SPMB with coupled REG-IV(+) ENVs or DRHS11(+) ENVs were stained with FITC-labeled antibodies against CD63 and the intensity values of the FITC and PE channels were measured by means of FCM. No CD63(+) events were detected in the FITC channel, whereas the increased signal intensity was evaluated through an analysis of the CM-Dil-stained ENVs in the PE channel  contained particles of a smaller size; however, these were enriched with exosomal markers CD63, CD9, and HSP70. An increasing amount of albumin was detected, starting from fractions 11 and 12. Interestingly, CM-Dil staining resulted in a slight shift in the size and density of plasma particles detected by both NTA and dot-blotting. Thus, our results provided a set of characteristics to assist with the process of size exclusion chromatography in plasma and CM-Dil-stained plasma.By maintaining the general workflow of the procedure, the proposed technique could be easily modified by using different lipophilic or membrane-incorporating dyes and different methods of size-exclusive chromatography. The list of membrane-labeling compounds is constantly growing ,20 and iDue to its exceptional sensitivity, the proposed approach can be used to analyze a minor fraction of circulating plasma ENVs. It is now assumed that the population of plasma ENVs is extremely heterogenous. It appears to contain vesicles derived from blood cells, the endothelium, and other tissue. However, the exact tissue origin of circulating ENVs has not yet been investigated, nor has the physiological composition of the plasma vesicular population. It may be supposed that changes in the vesicular composition of plasma may reflect various diseases and may serve as important diagnostic criteria. Moreover, the development of a well-differentiated tumor may be associated with an increased amount of specific tissue-derived ENVs in plasma. The sensitive detection of subtle changes in plasma vesicular composition could provide a new approach to the early diagnosis of cancer. The sensitive detection of tumor-derived ENVs with a specific cancer-associated pattern of surface markers can be studied as a method of disease monitoring during the course of therapy. Thus, the proposed technology is sensitive enough to serve as a new tool for accurate plasma ENV analysis and is fairly easy to use in clinical practice.


P < 0.01). NR_028138.1 was used to establish a BD diagnostic model . Verification in the second cohort revealed uniformly significant differences in NR_028138.1 (P < 0.0001). This study constructed a ceRNA regulatory network and provided a hypothesis for the pathogenesis of BD. NR_028138.1 was identified as a central element involved in the transcriptional regulation in BD and a potential biomarker.The pathogenesis of bipolar disorder (BD), a chronic mood disorder, is largely unknown. Noncoding RNAs play important roles in the pathogenesis of BD. However, little is known about the correlations of long noncoding RNAs (lncRNAs) with BD. Illumina high-throughput sequencing in BD patients and normal controls was used to identify differentially expressed (DE) genes. Two-step real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to validate DE-RNAs in the first cohort (50 BD and 50 control subjects). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and lncRNA-mRNA coexpression and lncRNA-microRNA (miRNA)-messenger RNA (mRNA) competing endogenous RNA (ceRNA) network analyses were used to predict the functions of DE-RNAs. Receiver operating characteristic (ROC) curve analysis and logistic regression were applied to evaluate diagnostic performance in an additional testing group (80 BD and 66 control subjects). A total of 576 significantly DE-lncRNAs and 262 DE-mRNAs were identified in BD patients, and 95 lncRNA-miRNA-mRNA interactions were used to construct a ceRNA regulatory network. Analysis of the first cohort showed that six RNAs  were differentially expressed in the BD group ( The incidence rate of BD in China has increased up to 2%, and the number of people with BD has reached more than 8 million. BD is characterized by complex clinical manifestations, difficult treatment, poor prognosis, and high risk of suicide2. Therefore, BD has become a critical social public health issue that needs to be addressed. It is urgent to explore the biomarkers that are able to predict the key factors important for BD occurrence and therapy.Bipolar disorder (BD) is a mood disorder caused by genetic and environmental factors6. Polesskaya et al. demonstrated in 2003 that lncRNA psza11q14 is associated with the pathogenesis of schizophrenia detected by autopsy of the brain tissue. The expression of psza11q14 in patients with schizophrenia is significantly decreased compared with that in the control subjects. Subsequently, the authors confirmed that psza11q14 expression is decreased in Brodmann areas 21, 22, and 9 and in the hippocampus in 36 schizophrenic patients compared with that in 35 normal control subjects. Functional experiments showed that psza11q14 is located on 11q14 within the first intron region of the dlg-2 gene and is transcribed in the opposite direction to dlg-2; moreover, dlg-2 plays an important role in the regulation of the N-methyl-D-aspartate (NMDA) receptor7. Issler et al. demonstrated that LINC00473 is a potential risk factor for enhanced susceptibility to depression in females and that LINC00473 expression is downregulated in the prefrontal cortex (PFC) in females but not in males8. LINC00473 is related to 35 protein-coding genes, most of which are associated with depression. However, the expression patterns and functions of lncRNAs in the context of BD remain largely unknown.Accumulating evidence indicates that long noncoding RNAs (lncRNAs) participate in many central nervous system diseases (CNDs), acting as pathogenic factors, potential therapeutic targets, prognostic indicators, or biomarkers12, and a novel regulatory mode of the network is composed of ceRNA-miRNA-mRNA interactions. The regulatory mechanisms of ceRNAs play an important role in the pathogenesis of CNDs14. Wang et al. reported that lncRNA WT1-AS suppresses oxidative stress injury and apoptosis in Alzheimer's disease via inhibition of the miR-375/SIX4 axis15. However, only a few studies investigated ceRNA networks in BD.Several recent studies demonstrated that lncRNAs competitively bind to microRNAs (miRNAs) to form a competing endogenous RNA (ceRNA) networkTherefore, this study aimed to investigate differentially expressed lncRNAs (DE-lncRNAs) in BD patients versus control subjects and comprehensively analyze a lncRNA-miRNA-mRNA ceRNA network involved in BD. We also evaluated whether identified lncRNAs are promising peripheral biomarkers. These findings may provide potential targets for the development of novel diagnostic and therapeutic strategies against BD.This study was approved by the Ethics Committee of the Second Affiliated Hospital of Nanchang University, China. The recruitment procedure followed relevant guidelines. All donors and their family members gave informed consent for the collection and use of the samples in this study. The subjects were divided into two cohorts. The first cohort was used for RNA sequencing. Four outpatients and inpatients who met the diagnostic criteria of BD were recruited at Jiangxi Psychiatric Hospital (January to December 2017), and four healthy volunteers from the surrounding communities and schools were included as the control subjects. Healthy control subjects did not have past history of psychiatric or neurodegenerative disorders, intellectual disability, or cancer. The second cohort was used for clinical validation. The subjects were recruited similar to the first cohort (January 2017 to December 2020). A total of 130 BD patients and 116 normal control subjects were recruited. The inclusion criteria were as follows. (i) The patients were diagnosed by two psychiatrists based on the Structured Clinical Interview for DSM-IV. (ii) The age of the patients was between 16 and 45 years. (iii) The patients did not receive any psychotropic medications  for at least four weeks. (iv) Healthy controls did not have any underlying diseases .Total RNA was extracted from peripheral blood using a PAXgene blood RNA kit  according to the manufacturer’s instructions. RNA was quantified using a NanoDrop 2000 spectrophotometer , and the RNA integrity number (RIN) was estimated using an Agilent 2100 bioanalyzer . Samples with RIN ≥ 7 were considered adequate for subsequent analysis. Two micrograms of RNA per sample was used for lncRNA library construction by a TruSeq Stranded Total RNA kit with Ribo-Zero Gold . Briefly, ribosomal RNA was removed using a Ribo-Zero Gold rRNA removal kit (Illumina), and rRNA-free RNA was reverse transcribed into complementary DNA (cDNA). After ligation of the terminal adapters, PCR amplification, and purification, the quality of the library was assessed using an Agilent bioanalyzer 2100 system. Then, the libraries were sequenced on the Illumina sequencing platform (HiSeq X Ten) with PE150 according to the manufacturer’s protocol. All data analysis was performed by OE Biotech Co. Ltd. .16. Genes with q-values ) lower than 0.05 and fold-changes higher than 2.0 were considered differentially expressed. Volcano plots and heatmaps of DE-RNAs were generated by R software (https://www.r-project.org/).DE-mRNAs and DE-lncRNAs in the BD group versus the control group were analyzed using DEGseq 2 packagehttp://www.geneontology.org) term analysis and Kyoto Encyclopedia of Genes and Genomes  pathway analysis. Corrected P-values < 0.05 indicated significant enrichment by the genes targeted by DE-lncRNA.Functional annotation of the genes targeted by DE-lncRNAs was performed by Gene Ontology . PCC > 0.95 or < -0.95 and http://www.mirdb.org/), TarBase V8 (http://microrna.gr/tarbase/), and TargetScan7.1 (http://www.targetscan.org/vert_71/) databases to predict target mRNAs. Third, target mRNAs were intersected with DE-mRNAs in the DE-lncRNA—DE-mRNA coexpression network. Finally, these interactions were combined to construct a lncRNA-miRNA-mRNA ceRNA network by Cytoscape v3.7.1 software (https://cytoscape.org/)17.RNAs in the DE-lncRNA—DE-mRNA coexpression network were used to construct a ceRNA network. A lncRNA-miRNA-mRNA network was generated as follows. First, DE-lncRNAs were input into the miRDB database to predict target miRNAs. Second, target miRNAs were transferred into the miRDB (https://www.ncbi.nlm.nih.gov/tools/primer-blast/) and synthesized by TsingKe Biotech. The expression levels of lncRNAs were normalized to the expression levels of the reference β-actin gene and were calculated using the 2-ΔΔCt method18.Real-time PCR was performed using a PCR thermocycler  and SYBR Green fluorescence dye according to the manufacturers’ protocol. The sequences of lncRNA-specific primer were designed using Primer BLAST , R software, and GraphPad Prism 7, and a value of Receiver operating characteristic (ROC) curves were used to assess specificity and sensitivity of lncRNAs for differentiation of BD patients from the control subjects based on area under the ROC curve (AUC). The cutoff values for optimal diagnostic points of the ROC curve were calculated at the largest Youden’s index (sensitivity + specificity − 1). Binary logistic regression and ROC curves were used to improve diagnostic efficiency.P > 0.05).The samples of four patients with BD and four control subjects were used for high-throughput sequencing, and 100 (50 BD and 50 control subjects) and 146 (80 BD and 66 control subjects) subjects were used for separate qRT-PCR analyses. Clinical characteristics of all recruited subjects are summarized in Table 2FC|≥ 1 and P-value ≤ 0.05. A total of 316 DE-lncRNAs and 97 DE-mRNAs were upregulated, and 260 DE-lncRNAs and 165 DE-mRNAs were downregulated. Volcano plot of DE-lncRNAs and DE-mRNAs are shown in Fig. We used the DEG-seq R package to determine the differences in the expression levels of lncRNAs and mRNAs between the BD and normal groups. A total of 576 significantly DE-lncRNAs and 262 DE-mRNAs were identified with the threshold of |logGO and KEGG enrichment analyses were used to assess and predict potential functions of identified DE-lncRNAs. Top 30 GO terms are shown in Fig. KEGG pathway analysis revealed top 20 pathways related to potential target genes of DE-lncRNAs , and 62 miRNAs in these pairs were associated with 39 DE-mRNAs in the coexpression network, forming a ceRNA network, which was composed of 39 mRNAs, 20 lncRNAs, and 62 target miRNAs and included 71 lncRNA-miRNA-mRNA ceRNA circular pathways Fig. . TCONS_0qRT-PCR analysis of 6 selected DE-RNAs, including 3 lncRNAs with the highest number of interactions in the ceRNA network  and 3 coexpressed mRNAs  in the BD and normal groups was used to validate associations between BD and the ceRNA pathways. As shown in Fig. P < 0.0001)  , with sensitivity and specificity values of 90% and 85%, respectively ; the results indicated a significant upregulation in BD patients compared with that in the control subjects (01) Fig. A. NR_028ely Fig. B.Figure P < 0.004, 95% CI: 0.830–0.999). We also constructed a ceRNA regulatory network to generate a hypothesis for pathogenesis of BD. NR_028138.1 was identified as a hub gene that participates in BD pathogenesis and can serve as a potentially useful diagnostic tool.The present study identified 576 significant DE-lncRNAs and 262 DE-mRNAs by high-throughput sequencing and confirmed that six RNAs  were significantly upregulated in BD patients compared with those in the control subjects. NR_028138.1 was correlated with BD, and AUC corresponding to a BD diagnostic model was 0.923 . This diagnostic model was confirmed in 70 individuals in the second step of the validation and had AUC 0.890 . RNA sequencing data and miRNA target prediction software miRDB and TargetScan were used to construct a ceRNA regulatory network to determine potential functions of NR_028138.1. These results suggested that NR_028138.1 was significantly and independently associated with BD.Recently, certain involvement of various lncRNAs, such as lnc-MALAT1 and apoptosis-related lncRNAs , was reported in BD26. TNF-α is a cytokine composed of 157 amino acids, is produced after injury in macrophages, lymphocytes, neutrophils, and structural cells, and is stimulated by inflammation or infection. TNF-α is considered a proinflammatory molecule that enhances the immune response and helps to accelerate elimination of the pathogens. TNF-α can induce apoptosis in combination with its receptor TNFR1 in neurons and microglia. The number of glial cells in the prefrontal cortex is significantly decreased in bipolar patients. Additional necrosis and apoptosis are observed in oligodendrocytes of the frontal cortex and caudate nucleus of bipolar patients. These phenomena may be related to increased expression of TNF in the central nervous system of BD patients. Therefore, TNF is an important molecular target in the pathogenesis of BD. NR_028138.1 was colocalized with TNF, indicating that NR_028138.1 may participate in the development of BD by regulating the expression of TNF. Specific regulatory mechanism of this process needs further experimental verification.NR_028138.1 was coexpressed with 15 DE-mRNAs . Overall, high PCCs between these RNAs represented a high possibility of similar functions. BD is known to be associated with changes in neural plasticity and neuronal survival. These processes are influenced by many factors, including synergistic effects of neurotransmitters, hormones, neurotrophic factors, and inflammatory mediators. Vares et al. showed an increase in the levels of inflammatory chemokines, including tumor necrosis factor (TNF)-α, interleukin (IL)-1β, and IL8, in euthymic BD patients compared with those in the healthy control subjectsLncRNAs can be used as miRNA sponges to regulate protein-coding RNAs during the course of various diseases, such as schizophrenia, depression, and anxiety disorder. A ceRNA network for BD was constructed in the present study based on 20 lncRNAs, 39 mRNAs, 62 miRNAs, and 71 lncRNA-miRNA-mRNA pathways. lncRNAs and mRNAs were coexpressed in each ceRNA pathway and interacted with same miRNAs. In this network, NR_028138.1 participated in 19 ceRNA pathways, which contained 12 miRNAs and 15 mRNAs, and one of the pathways was NR_028138.1 → hsa-miR-5196-5p → TNF. Hence, we hypothesize that NR_028138.1 regulates TNF by binding to hsa-miR-5196-5p.Some advantages and limitations of the present study should be acknowledged. First, we identified DE-lncRNAs in BD patients versus the control subjects and comprehensively analyzed a ceRNA network involved in BD. Second, NR_028138.1 can be used as a diagnostic marker for BD, and robustness of the diagnostic model was verified externally. Third, we used bioinformatics to determine genes and networks that cause the disease. The limitations are as follows: First, we sampled peripheral blood lncRNAs but did not collect the samples from the central nervous system. Second, although we strictly controlled for other mental disorders, we did not control for common diseases, such as diabetes, hypertension, and metabolic diseases, which could have influenced the correlations between lncRNAs and BD. Third, the number of the samples, particularly in the BD group, limited statistical power of microarray analysis. Fourth, the present study focused only on the regulatory mechanism of ceRNA; however, lncRNA-related regulation is complex. Therefore, future studies should analyze all DE-lncRNAs in a larger cohort and explore other regulatory mechanisms. Finally, the results of the bioinformatic analysis should be verified in in vivo and in vitro experiments.In conclusion, the present study confirmed that the level of NR_028138.1 in peripheral blood of patients with BD was significantly higher than that in the normal control subjects, and NR_028138.1 was identified as a potential biomarker of BD. Additionally, we successfully constructed a lncRNA-miRNA-mRNA ceRNA network, which was helpful to elucidate the pathogenesis of BD and crosstalk between lncRNAs and mRNAs.


P2RY14 in HNSC patients. In addition, the survival analysis showed that HNSC patients with high expression had longer survival than patients with low expression from both TCGA databases and our own patients. We further discovered that P2RY14 is involved in the immune activity in the TME of HNSC; a downregulation of P2RY14 resulted in being an indicator for the conversion of TME status (from immune-dominant to metabolic-dominant status). The intersection analysis of genes co-expressed with P2RY14 indicated that the T-cell receptor signaling pathway and PD-L1 expression and PD-1 checkpoint pathway were candidate signaling pathways driven by the P2RY14 gene in HNSC. Further investigation of immune-associated signaling pathways regulated by P2RY14 may help HNSC patients gain higher immunotherapy benefits.The tumor microenvironment (TME) has a crucial role in tumor development, progression, and treatment response. Yet, the exact interaction between cancer biology and the TME is not fully understood. The following study analyzed the correlation between immune/stromal/estimate scores and survival prognosis in head and neck squamous cell carcinoma (HNSC) using a bioinformatic method. As a result, a predictive biomarker, UDP-glucose-specific G(i) protein-coupled P2Y receptor (P2RY14), was discovered. The potential role of P2RY14-driven signaling pathways in the immune-remodeling of TME was then investigated. Briefly, low immune scores were associated with unfavorable prognosis and clinical-stage, larger tumor size, and the down-regulation of  Head and neck squamous cell carcinoma (HNSC) is one of the most common malignancies of the head and neck region and a sixth leading cancer worldwide . More thThe tumor microenvironment (TME) has a crucial role in tumor development, progression, and treatment response. This environment includes the surrounding blood vessels, immune cells, fibroblasts, signaling molecules, and the extracellular matrix . StudiesP2RY14) is a protein-coding gene that has an important role in signaling by G-protein-coupled receptors (GPCR) and peptide ligand-binding receptors. In stem/progenitor cells, P2RY14 inhibits cell senescence by monitoring and responding to the extracellular manifestations of tissue stress (UDP-glucose-specific G(i) protein-coupled P2Y receptor  in HNSC patients. Finally, Gene Set Enrichment Analysis (GSEA) was used to explore the molecular mechanism of P2RY14 in the immune modulation of head and neck cancer.Over the last two decades, several genome-wide gene expression repertoires, such as The Cancer Genome Atlas (TCGA), have been established to explore the impact of tumor genetic composition on clinical outcomes . In this1. The samples’ stromal/immune/estimate scores were calculated using the ESTIMATE algorithm2.RNA-seq data of 543 HNSC cases  and corresponding clinical data  were downloaded from the cBioPortalP2RY14. The survival curve was drawn using the “survminer” and “survival” R packages. This relationship was verified using a log-rank test. The p-value < 0.05 was considered to be statistically significant  curve analysis was used to determine the cut-off score of ImmuneScore and StromalScore and the cut-off expression level of nificant . These a2(fold change)| > 4, false discovery rate (FDR) < 0.05. Heat maps of differential genes were drawn using the “pheatmap” R-package.Four hundred ninety-nine tumor samples were classified into a high score or low score group, depending on the comparison to the cut-off score regarding ImmuneScore and StromalScore. Differential expression analysis was performed on the count matrix of the sample using the “limma” R-package. The screening conditions for the differential genes were performed as follows: |logR language was used to perform functional enrichment analysis of the differentially expressed genes (DEGs), including Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). PPI network was constructed using the Cytoscape software. The top 30 genes ordered according to the number of nodes  are shown in the plot .p-value from small to large in univariate COX are shown in the plot.R-language loaded with package survival was used for univariate COX regression. The top 30 genes ordered by Oral squamous cell carcinoma (OSCC) patients who underwent surgical resection at the Peking University School and Hospital of Stomatology during 2014–2016 were enrolled in the current study. Patients with diabetes, hyperthyroidism, and other metabolic diseases were excluded. Finally, 94 patients  were included in the study. Telephone follow-up was performed to analyze the survival trends among these patients.The Ethics Committee of Peking University School and Hospital of Stomatology (NO. PKUSSIRB-201839134) approved this study. Informed consent was obtained from all patients.The tissue sections of 94 OSCC patients were collected, deparaffined, and rehydrated in xylene and a gradient ethyl alcohol series. Antigen retrieval was performed in 0.01 M sodium citrate buffer (pH = 6.0) and the samples were treated with 3% (w/v) hydrogen peroxide for 10 min. Anti-P2RY14 antibody  and horseradish peroxidase-labeled secondary antibody  were then used for staining. All images were analyzed with an optical microscope (Olympus). Five representative fields separated over the tumor tissue were randomly selected for statistical analysis.P2RY14 in TCGA HNSC tumor samples and nine kinds of TICs.CIBERSORT computational method was used for the estimation of the TIC abundance profile. R language was used to perform the correlation analysis between the mRNA expression data of p < 0.05, FDR q < 0.25 was considered as significant (Kyoto Encyclopedia of Genes and Genomes pathway gene set (c2.cp.kegg.v7.2.symbols.gmt) was used as the target sets. The software gsea-4.1.0 was downloaded from Broad Institute to perform the analysis. The whole transcriptome of all tumor samples was used for GSEA. Only the gene sets with NOM nificant .P2RY14 expression level among the different clinical/T/N stages from all patients. A p-value < 0.05 was considered to be statistically significant.The Kaplan–Meier method was used for survival analysis. One-way ANOVA was used to compare the p = 0.020 in the log-rank test); while no difference was observed between stroma and estimate score groups (low vs. high)  .P > 0.05). When considering the tumor size, the score decreased with the tumor size, and the relationships between the median immune scores of T1 and T3, T1 and T4, T2 and T3, T2 and T4 were statistically significant , namely: N1 > N3 > N2 > N0 (P > 0.05). This data suggested a strong correlation between immune scores and the tumor sizes in the TNM staging system of the HNSC patients.Next, the correlation between the immune scores and the clinical factors of HNSC patients was analyzed to explore how immune scores affect HNSC patients’ prognosis. When considering clinical staging (T), the median immune scores in stage I were the lowest while they were the highest in stage II; the order of immune scores was: StageII > StageIII > StageIV > StageI, but the difference was not statistically significant  was performed to examine the relationship between the stromal and/or immune scores and the gene expression profile of the samples. The heat map of the stromal/immune scores (low vs. high group) revealed differential gene expression profiles between the samples: stromal scores: 954 genes were up-regulated, and 90 were down-regulated; immune scores: 881 were up-regulated and 148 genes were down-regulated . As the Next, functional enrichment analysis, including BP, CC, MF using GO, and the analysis of the signaling pathways using KEGG, were performed. GO functions were mainly enriched in extracellular matrices, and carbohydrate or GPCR binding, and inflammatory and immune responses , while Kp-value of univariate COX regression was carried out. Only three factors  were overlapping from the above analyses  protein-coupled P2Y receptor (P2RY14) were suggested as prognostic factors in HNSC datasets  and poor prognosis, thus suggesting that P2RY14 might be a potential prognostic marker for HNSC.Immune checkpoint inhibitors, pembrolizumab, and nivolumab, which can hinder the inhibitory interaction between programmed cell death protein 1 (PD-1) and its ligand PD-L1, have been explored in patients with advanced/metastatic HNSC . It was P2RY14 expression is down-regulated in lung cancer tissues . The patients/participants provided their written informed consent to participate in this study.QL, LX, YG, and CG wrote, conceived, and designed the experiments. RY and QQ collected the samples. QL, LX, YL, and YG analyzed and interpreted the data. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Delirium screening instruments (DSIs) should be used to detect delirium, but they only show moderate sensitivity in patients with neurocritical illness. We explored whether, for these patients, DSI validity is impacted by patient-specific covariates.Data were prospectively collected in a single-center quality improvement project. Patients were screened for delirium once daily using the Intensive Care Delirium Screening Checklist (ICDSC) and the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU). Reference was the daily assessment using criteria from the Diagnostic and Statistical Manual, 4th Edition, Text Revision (DSM-IV-TR). In a two-step receiver operating characteristics regression analysis adjusting for repeated measurements, the impact of acute diagnosis of stroke or transient ischemic attack (TIA), neurosurgical intervention, Richmond Agitation Sedation Scale, and ventilation status on test validity was determined.p = 0.029) and the CAM-ICU in its severity scale version (p = 0.004). Neither acute diagnosis of stroke or TIA nor neurosurgical intervention or mechanical ventilation significantly influenced DSI validity.Of 181 patients screened, 101 went into final analysis. Delirium incidence according to DSM-IV-TR was 29.7%. For the first complete assessment series , sensitivity for the CAM-ICU and the ICDSC was 73.3% and 66.7%, and specificity was 91.8% and 94.1%, respectively. Consideration of daily repeated measurements increased sensitivity for the CAM-ICU and ICDSC to 75.7% and 73.4%, and specificity to 97.3% and 98.9%, respectively. Receiver operating characteristics regression revealed that lower Richmond Agitation Sedation Scale levels significantly impaired validity of the ICDSC (The CAM-ICU and ICDSC perform well in patients requiring neurocritical care, regardless of the presence of acute stroke, TIA, or neurosurgical interventions. Yet, even very light or moderate sedation can significantly impair DSI performance.The online version contains supplementary material available at 10.1007/s12028-021-01319-9. Significantly more patients in the delirium group were mechanically ventilated  with longer duration . Patients in the delirium group had a longer ICU length of stay , higher in-hospital mortality  and spent more days with sedation . Sedation was defined as at least light sedation (RASS − 2 or lower) during the assessment.Out of  = 80 44.% were exn = 15 (15%) patients were diagnosed positive for delirium according to DSM-IV-TR. The CAM-ICU showed a sensitivity of 73.3%, a specificity of 91.8%, a PPV of 61.1%, and a NPV of 95.1%. The ICDSC had a sensitivity of 66.7%, a specificity of 94.1%, a PPV of 66.7%, and a NPV of 94.1% (Table p = 0.317). As shown in Fig. p = 0.993). The Pietra index was 0.50 for the ICDSC and 0.39 for the ssCAM-ICU. The difference in Pietra indexes indicates that the ICDSC has a better ability to distinguish between patients who were delirium-positive and delirium-negative.In the first complete assessment series, 1% Table . Test van = 324 corresponding DSM-IV-TR and CAM-ICU assessments were conducted, and for the ICDSC, n = 346 corresponding DSM-IV-TR and ICDSC assessments were conducted. For the CAM-ICU, the logistic regression model with random effects revealed a sensitivity of 75.7%, a specificity of 97.3%, a PPV of 52.8%, and a NPV of 98.9% (Table p = 0.465). The ability of the DSIs to distinguish between patients who were delirium-positive and delirium-negative as measured with the Pietra index was 0.62 for the ICDSC and 0.46 for the ssCAM-ICU. Just like for the first complete assessment series, the difference in Pietra indexes indicates that the ICDSC has a better ability to distinguish between patients who were delirium-positive and delirium-negative.For the CAM-ICU, p = 0.029), and the ssCAM-ICU . Thus, higher RASS scores were associated with better test validities of the ICDSC and the ssCAM-ICU. Figure As shown in Tables In this single-center QI project, we prospectively assessed the diagnostic validity of the most common DSIs CAM-ICU and ICDCS in a neurocritical care population. Taking into account single and multiple testing on one patient, we revealed a moderate to good sensitivity and an excellent specificity in this patient cohort. In a second step, we examined the influence of neurocritical care-specific and general covariates on the diagnostic validities of the CAM-ICU and ICDSC, which revealed a significant influence of the RASS score on the diagnostic performance of the DSIs. Neither acute diagnosis of stroke/TIA or neurosurgical intervention, nor the general covariates sex, age, or mechanical ventilation had a significant impact on DSI validities.The delirium rate of 29.7% found in this QI project lies within the expected range in patients with neurocritical illness. Previous trials reported a delirium prevalence ranging from 11.8 to 45.9% , 25–27. Regarding the diagnostic validity of the DSIs, our findings are in line with a recently published observational study of 123 neurocritical care patients , which rPatel et al.  identifiThis work adds to current literature that DSI validity is influenced by the same covariates in neurocritical care patients as in medical/surgical critical care patients , 29, namThis QI project has several limitations. Our patient cohort consisted of a heterogeneous group of patients with neurocritical illness. On the one hand, this can be considered a strength as it does not limit the results to one specific neurocritical care patient subgroup, on the other hand, our study might underestimate the impact of covariates in individual subgroups. Because the project results were in line with previous works, this limitation might be mitigated. In addition, a larger sample size would have increased the precision of our estimates and the statistical power to detect a significant influence of covariates on DSI validity. However, our sample size was comparable to other DSI validation studies –16, 29. In summary, delirium screening in patients with neurocritical illness performs with an adequate diagnostic validity, irrespective of the presence of acute stroke, TIA, or neurosurgical interventions. However, even a very light sedation level significantly impairs DSI performance. Notably, a false positive DSI in neurocritical care patients might be due to different differential diagnoses than that in the medical or surgical critical care population.Supplementary file1 (DOCX 22 KB)Supplementary file2 (DOCX 261 KB)Below is the link to the electronic supplementary material.


Caenorhabditis elegans nematodes. The influence of the modification agent as well as the particles’ composition on physicochemical properties of hybrid nanomaterials was investigated. Several microscopy techniques, such as fluorescence and dark-field microscopy, were compared in monitoring the distribution of nanomaterials in nematodes’ organisms. The effects of QDs-halloysite composites on the nematodes’ life cycle were investigated in vivo. Our fluorescent hybrid probes induced no acute toxic effects in model organisms. The stable fluorescence and low toxicity towards the organisms suggest that the proposed synthesis procedure yields safe nanoarchitectonic materials that will be helpful in monitoring the behavior of nanomaterials inside living cells and organisms.Halloysite is a promising building block in nanoarchitectonics of functional materials, especially in the development of novel biomaterials and smart coatings. Understanding the behavior of materials produced using halloysite nanotubes within living organisms is essential for their safe applications. In this study, quantum dots of different compositions were synthesized on the surface of modified clay nanotubes, and the biodistribution of this hybrid material was monitored within  A nanoarchitectonics approach for targeted production of biomaterials, catalysts, membranes, sensors and smart coatings based on halloysite is a fast-developing area of research ,2,3,4,5.Scientific and industrial applications motivate the collection of more data on halloysite-based nanomaterials toxicity and behavior within living organisms ,13,14. PCurrently, just a handful of papers report on the cytotoxicity of semiconductor nanoparticles stabilized on halloysite and even less are based on the studies using the clay-derived nanomaterials in vivo ,19,20. OCaenorhabditis elegans [Quantum dots have a great advantage due to composition-dependent optical properties, such as fluorescence emission spectra, quantum yield and stability. Free QDs have been extensively investigated recently. The CdS, ZnS, and CdSe free QDs, showed to be toxic to  elegans . NanoarcC. elegans is a free-living soil nematode with a body length of 1 mm and a width of 70–90 µm, which is frequently used as a model organism for studying various processes in biology, including energy metabolism, immunity and aging. Reproduction rate, optical transparency of the body, the short life cycle (3 days), the short life span (2–3 weeks), being not expensive, and relatively easy cultivation in a laboratory make this nematode species an ideal model organism [C. elegans genes are homologous to human genes [C. elegans nematodes are considered as an important alternative in vivo model system for laboratory studies of toxic effects and elucidation of the fundamental mechanisms of the formation of toxicity in engineering nanomaterials [organism . Importaan genes . For thian genes , Parkinsan genes , diabetean genes , Duchennan genes  and cancan genes . In addiaterials ,33,34.C. elegans nematodes. Their physicochemical properties were investigated in relation to composition and synthesis procedure. The toxicity of this material was studied in vivo. Fluorescent imaging was compared to the enhanced dark-field hyperspectral imaging (EDF-HSI), which combines dark-field microscopy (DEM) and hyperspectral imaging (HSI), to monitor the nanomaterials inside living organisms.Microscopy imaging can reveal the uptake efficiency, dispersion state and potential cytotoxicity of nanomaterials. Fluorescence imaging using wide-field fluorescence or confocal laser scanning microscopy is among the most commonly used methods for visualization and tracking of nanomaterials, providing dynamic and real-time information of the interactions between nanomaterials and organisms. In this work, quantum dots with different compositions synthesized on the surface of modified halloysite nanotubes were used for monitoring of the distribution of clay inside 5H4O2), (3-aminopropyl)triethoxysilane (APTES), cadmium nitrate tetrahydrate (Cd(NO3)2·4H2O) and zinc nitrate hexahydrate (Zn(NO3)2·6H2O) were purchased from Sigma-Aldrich , while thioacetamide (TAA), ammonium hydroxide solution (NH4OH) and 96% ethanol and hydrazine hydrate (N2H4) were all purchased from Acros.Halloysite (HNT), f Furan-2-carbaldehyde hydrazine (azine) on the surface of clay nanotubes.0.7Zn0.3S QDs on the surface of clay tubes, the obtained HNT-NH2 or HNT-Azine was dispersed in a Cd(NO3)2 or Cd(NO3)2/Zn(NO3)2 ethanolic solution of calculated concentration and kept in an ultrasonic bath for 30 min. Then the TAA ethanolic solution with concentration calculated, taking into account a S/Me molar ratio of 1, was added, and the pH was adjusted to 10 with NH4OH. The reaction time was 5 min. After the reaction, the precipitate was centrifuged, washed several times with ethanol and dried at 60 °C for 24 h. The resulting samples were named HNT-NH2-CdS, HNT-Azine-CdS or HNT-Azine-Cd0.7Zn0.3S. The scheme of synthesis is shown in For the synthesis of CdS or CdThe morphology of the nanomaterials was studied using transmission electron microscopy (TEM) . To analyze the elemental compositions of the samples, inductively coupled plasma mass spectrometry (ICP-MS) was used . The crystalline structure was analyzed with X-ray phase analysis . The analysis was carried out using Cu Kα radiation . Spectral characterization was carried out with UV-Vis diffuse reflectance spectra analysis in the range of 350–800 nm with 1 nm of resolution . The fluorescence spectra of nanomaterials were measured from a solid sample using a fluorometer  at room temperature in a region from 200 to 600 nm. Zeta-potential measurements were conducted using nanoparticles size analyzer SZ-100 .Escherichia coli OP50 bacteria as a food source. The sterile eggs were inoculated to the nutrient agar media, and the animals were cultivated to reach the adult hermaphrodite developmental stage. To consider the efficiency of the delivery of nanoparticles and their toxicity, we evaluated certain physiological parameters, such as body length and reproductive potential . Both body length and fertility were determined using optical microscopy images of adult nematodes at certain times, as indicated. An Olympus BX51optical microscope equipped with CytoViva dark-field condenser was used to study the distribution of nanoparticles in nematodes. Optical bright-field and epifluorescence microscopy experiments were conducted using a Carl Zeiss Imager Z2 upright optical microscope equipped with apochromatic objectives . Fluorescence imaging was performed using Carl Zeiss HXP 120 C excitation light source and Carl Zeiss fluorescence FITC narrow band cube . Images were captured using an HRC CCD camera (Carl Zeiss). Nematodes were cultivated for 3 days on 6 lunar planets in a solid medium with the addition of the various studied samples of nanotubes  at a concentration of 1 mg/mL (0.5 mg/mL). The pristine halloysite nanotubes were used as the reference sample.Nematode cultures  were groThe spectral characteristics of quantum dots are outlined by their composition and structure; therefore, nanoarchitectonics of semiconductors and semiconductors-based hybrid materials is important for research and industrial applications. Currently, the synthesis of QDs of different compositions in situ on the surface of inorganic nanomaterials and investigation of their properties, including in vivo studies, is poorly studied. Herein, based on the QDs-halloysite composites, we show a novel approach for the synthesis of nanostructured hybrid photonic materials and potential applications of such probes in monitoring nanomaterials in vivo.0.7Zn0.3S/HNT-Azine and CdS/HNT-NH2 was performed according to a procedure described in [0.7Zn0.3S were synthesized in situ on the surface of modified clay following a simple precipitation method. We demonstrate that this kind of synthesis procedure led to the self-organization of sulfide particles with less than 10 nm size selectively on the surface of the clay and not in the bulk. Such materials are quite stable and have good water dispersibility.The synthesis of CdS/HNT-Azine, Cdribed in   lattice plane shifted to the higher diffraction angles (44.8°), indicating the formation of Cd0.7Zn0.3S solid solution [2 refractogram had Bragg peaks at 2θ = 43.21 and 52.34°, corresponding to cubic CdS lattice.TEM investigation revealed that all samples exhibited a homogeneous distribution of QDs on the surface of modified clay tubes a. Due tosolution . CdS/HNT0.7Zn0.3S/HNT-Azine, and CdS/HNT-NH2, respectively. Zn content was 0.9 wt.% in Cd0.7Zn0.3S/HNT-Azine. This is in good correlation with the calculated amounts of metals used during the synthesis procedure.Elemental composition analysis was performed using ICP-MS. Cd content was found to be 3.5, 3.2, and 3.9 wt.% in CdS/HNT-Azine, Cd0.7Zn0.3S/HNT-Azine, and CdS/HNT-NH2.Zeta-potential measurements were performed at each reaction stage. We found that successful surface modification with APTES changed zeta-potential for pristine halloysite from −45 to +12 mV. Modification with azine resulted in a slight change in this value to −25 mV. HNTs-QDs composites were characterized by zeta-potential values of −30, −32, and −20 mV in CdS/HNT-Azine, Cd2, CdS/HNT-Azine, and Cd0.7Zn0.3S/HNT-Azine, respectively. The band gap of a bulk CdS is 2.4 eV [0.7Zn0.3S/HNT-Azine was due to the formation of solid Cd0.7Zn0.3S solution. The addition of Zn in various concentrations increases a band gap of mixed sulfide [Spectral characteristics of QDs-halloysite composite were analyzed to investigate the influence of the composition to the properties of QDs stabilized on clay tubes. A broad absorption region below 500 nm in all samples was attributed to the charge transfer from the valence to the conduction band of QDs a. UV-Viss 2.4 eV . A sligh sulfide .C. elegans as a model organism. First, we investigated the biodistribution of the clay nanotubes in the nematodes, as shown in C. elegans not only through the oral apparatus but also through the vulva, from where they diffuse into other organs [In vivo studies of QDs-halloysite composites were performed using r organs .The distribution of quantum dots stabilized on the surface of halloysite nanotubes inside the intestine of nematodes is shown in fluorescence photographs as bright blue clusters in the pharynx and tail b. In the0.7Zn0.3S, it has a higher light adsorption. Halloysite UV-Vis diffuse reflectance analysis showed that it reflects more than 80% of light in UV and visible regions [The dark-field images a confirm regions . Despite2-CdS, HNT-Azine-CdS, and HNT-Azine-Cd0.7Zn0.3S did not affect the reproductive ability of nematodes. By comparing the growth in body length of C. elegans nematodes exposed to CT, we can determine the sensitivity to toxins and toxicity at an early stage of exposure. As can be seen from We then investigated some physiological parameters in nematodes exposed to CdS/HNT. We found that long-term exposure (from stage L1 to an adult) with the nanotubes does not cause abnormalities in the reproductive organs of nematodes associated with the appearance of the BOW phenotype, in which fertilized embryos hatch inside the mother’s body and begin to feed on its tissues . In the C. elegans. It has been shown that using fluorescent probes with stable fluorescence, it is possible to study the distribution of clay tubes that are extensively studied as drug delivery polymer-composite systems, in food polymeric packages, and other polymer materials. This approach may help to obtain new data on such important issues as nanomaterials toxicity, cells uptake, and the traveling of nanoparticles within a body. It has been stated that these fluorescent QDs stabilized on halloysite nanotubes showed no acute toxic effects on reproduction ability, life cycle, and behavior of model nematodes. It is especially worth noting that the nanomaterials were not found in the uterus, spermatheca, and nematode embryos.A new approach that may help in monitoring the behavior of nanomaterials inside living organisms by the anchoring of QDs on their surface was proposed in this study. Fluorescent quantum dots synthesized on the surface of modified clay nanotubes were used to study the distribution of halloysite clay tubes within the body of model nematodes


Acute myeloid leukemia (AML) refers to a heterogeneous group of hematopoietic malignancies. The well-known European Leukemia Network (ELN) stratifies AML patients into three risk groups, based primarily on the detection of cytogenetic abnormalities. However, the prognosis of cytogenetically normal AML (CN-AML), which is the largest AML subset, can be hard to define. Moreover, the clinical outcomes associated with this subgroup are diverse. In this study, using transcriptome profiles collected from CN-AML patients in the BeatAML cohort, we constructed a robust prognostic Cox model named NEST (Nine-gEne SignaTure). The validity of NEST was confirmed in four external independent cohorts. Moreover, the risk score predicted by the NEST model remained an independent prognostic factor in multivariate analyses. Further analysis revealed that the NEST model was suitable for bone marrow mononuclear cell (BMMC) samples but not peripheral blood mononuclear cell (PBMC) samples, which indirectly indicated subtle differences between BMMCs and PBMCs. Our data demonstrated the robustness and accuracy of the NEST model and implied the importance of the immune dysfunction in the leukemogenesis that occurs in CN-AML, which shed new light on the further exploration of molecular mechanisms and treatment guidance for CN-AML. Acute myeloid leukemia (AML) is a heterogeneous group of hematopoietic disorders with diverse clinical outcomes . The iniAdvances in sequencing technologies have contributed to an increased understanding of AML biology. Based on genetic abnormalities, the European Leukemia Network (ELN) risk stratification system classifies AML patients into three risk groups: favorable, intermediate, and adverse Table S1FLT3, NPM1, CEBPA, RUNX1, TP53, and ASXL1  as a training dataset. The cohort includes samples from both bone marrow and peripheral blood. On the one hand, bone marrow samples were derived from 105 patients with de novo CN-AML and 21 healthy donors. There were 33 samples in total derived from healthy donors. Among them, 19 samples were BMMCs, and the remaining 14 samples were CD34+ bone marrow (CD34+) cells. Notably, all CD34+ cells were collected from three patients. CD34+ sample from one patient was included in each sequencing batch . On the other hand, peripheral blood samples included 43 patients with de novo CN-AML in BeatAML. Moreover, to validate our model, we selected bone marrow data from four external validation datasets of CN-AML. Of these, GSE71014 (n = 104) (http://www.ncbi.nlm.nih.gov/geo/), and TARGET-AML (https://ocg.cancer.gov/programs/target). Apart from these datasets, we also download the TCGA-LAML . The sample origin of the TCGA-LAML was PBMCs. Due to the different treatment regimens and favorable outcomes of AML-M3 patients, we excluded them from all cohorts. Ensemble IDs from the BeatAML dataset were converted to gene symbol with a GTF file (Homo_sapiens.GRCh37.75.gtf) downloaded from GENCODE (https://www.gencodegenes.org/). For microarray datasets, the median value was regarded as the gene’s expression value for multiple probe sets corresponding to the same gene. The overall survival time and genetic mutation information were obtained from publications and the GEO database. No specific ethical approval is required for this study, as all datasets used were publicly available.We downloaded gene expression profiles (raw count) and clinical information of n = 104) , GSE1241n = 104)  were micRGET-AML  were genCGA-LAML  dataset The raw gene expression of the BeatAML dataset was normalized by the trimmed mean of M values (TMM) method with the “edgeR” package in the R platform . The vooWe used the “clusterProfiler” R package to perform Gene Ontology (GO) enrichment analysis . Moreove1 × β1 + expression of gene2 × β2 + … expression of genen × βn. We tested the proportional hazards assumption based on the scaled Schoenfeld residuals using the “survival” packages in the R platform.The gene expression data (raw count) were normalized with the TMM method. We got the normalized counts per million mapped reads (CPM) value. A log-based transformation (log(cpm+1)) value was used for subsequent survival analysis. Firstly, we used univariate Cox regression analysis and the log-rank test to detect prognosis-related DEGs. The cutoff value for univariate Cox analysis was 0.20, and the cutoff P-value for the log-rank test was 0.10. To ensure the biological significance of the identified DEGs, filtered genes whose highest expression value (log(cpm+1)) among CN-AML and healthy donors less than 1.0 were removed. Then, the BeatAML dataset was used as the training cohort to construct the prognostic Cox model. Least absolute shrinkage and selection operator (LASSO) analysis and stepwise algorithm were applied simultaneously to select the most significant prognostic gene from the identified prognosis−related DEGs. The optimal values of the penalty parameter λ were determined through ten folds cross-validation. The optimal tuning parameter λ was identified via the min criterion. A prognostic Cox model was established based on a linear combination of the gene expression level multiplied by a regression coefficient (β). The risk score of the model was calculated as follows: risk score = expression of geneThe risk score for each patient was calculated with constructed Cox model. Using the median of the risk score as the cutoff value, patients in each cohort were divided into high- and low-risk group. We applied a log-rank test to compare the overall survival difference between the high and low-risk group. Meanwhile, the time-dependent receiver operating characteristic (ROC) analysis was applied to calculate the area under the ROC curve (AUC) value at 1-, 2-, 3-years of the model. The AUC value of more than 0.5 indicates a non-random effect, and 1 indicating a perfect model . The GSEFirstly, we enumerated all possible combinations of 12 genes included in the model. Specifically, we selected from 3 to 12 out of 12 genes to construct a new multivariate Cox model. We got 4017 combinations in total. Next, for each combination we constructed a new multivariate Cox model with selected genes in BeatAML. Then, for each combination, the new model was applied to predict risk scores for CN-AML patients in GSE12417, TARGET and BeatAML, respectively. We calculated the 1-, 2-, 3-years AUC value and the log-rank test’s P-value in these cohorts. Combinations filtering was executed based on the following criteria: 1) the minimum value of 1-, 2-, 3-years AUC value should more than 0.60 ; 2) the We summed up all normalized AUC values in three independent cohorts in each combination and selected the combination with the largest AUC value. Finally, we constructed a new Cox model with the genes included in the combination with the largest AUC value.We screened publications from 2014 to 2020 on PubMed using the following keyword terms:  AND (“TCGA” OR “GEO” OR “biomarker” OR “prognosis” OR “prognostic”). We got three published models in total. The detailed model formulas were as follow: 1) MPG6 score = (0.0492 * CD52) - (0.0018 * CD96) + 0.0131 * EMP1) + (0.2058 * TSPAN2) + (0.0234 * STAB1) - (0.3658 * MBTPS1) 1 * EMP1 . We applhttp://www.r-project.org/).In our study, overall survival (OS) was defined as the time interval between the date of diagnosis and the date of death or lost to follow-up. We conducted univariate Cox analysis, and factors with P-value <0.10 were incorporated into a multivariate Cox analysis, which was used to construct a prognostic Cox model and to identify independent prognostic factors. All statistical analyses were performed with the R 3.6.1 software (de novo CN-AML patients from BeatAML (Methods). The ages of the CN-AML patients ranged from 2 to 84 years, a large proportion of which were older than 40 years (88.57%). No significant difference in the sex composition was observed. According to the ELN recommendations, 30% of the patients in the BeatAML cohort had a good prognosis, 26% had an intermediate prognosis, and 31% had an adverse prognosis, which implied prognostic heterogeneity among the CN-AML population. The spectrum of genetic mutations in the BeatAML cohort was broad , FLT3-TKD mutations (28.12%), and NRAS mutations (15.62%). Among the 33 samples from healthy donors, 19 samples were BMMCs samples and 14 samples were bone marrow CD34+ cells. All of CD34+ cells were collected from three healthy donors. Notably, CD34+ sample from a single donor was included in each sequencing batch, and this sample (Control_CD34) was sequenced 12 times in total. Control_CD34 served as a quality check against intergroup batch effects. Few batch effects were observed for the BeatAML dataset . After the initial screening from all DEGs, we identified 110 DEGs significantly associated with the clinical outcome  analyses , GSE71014 (n = 104), GSE6891 (n = 88), and TARGET (n = 26). The detailed demographic data for these cohorts are listed in Methods). After filtering, we obtained 20 candidate combinations. We then calculated a normalized AUC value to determine the optimal combination (see Methods), and we selected the combination highlighted by the red box, which presented with the largest normalized AUC value  and compared the performance using the BMMC datasets, including GSE12417, GSE71014, TARGET, and BeatAML. For these comparisons, we focused on two indicators: the P-value of the KM analysis (log-rank test), to evaluate each model’s ability to distinguish between patients with favorable and adverse prognoses, and the AUC value of each model, to reflect the accuracy. The AUC value of more than 0.5 indicates a non-random effect, and 1 indicating a perfect model.To further evaluate the performance of the NEST model, we compared our NEST model with other CN-AML prognostic models that were published from 2014 to 2020. These models included the MPG6 model , the 3-gThe survival analysis showed that the risk score predicted by our model was significantly correlated with the survival of the patients in three out of four cohorts , which is also a PBMC dataset. We selected CN-AML (n = 60) from the TCGA-LAML. Time-dependent ROC and KM analyses were applied to both datasets. No difference between the low- and high-risk groups was observed for either cohort  from the BeatAML dataset. Meanwhile, we downloaded the TCGA-LAML (n = 151) from the TCGA data portal , which could represent a critical early event that occurs during tumor metastasis in multiple cancers. Furthermore, they demonstrated that the decreased expression of C1orf116 was associated with poor prognosis in lung and prostate cancer patients, which is consistent with our results in CN-AML. DNTT has been reported to play important functional roles in VDJ recombination and T cell receptor (TCR)  (TFCP2L1 is a downstream target of the leukemia inhibitory factor (LIF)/signal transducer and activator of transcription (STAT3) pathway, which mediates self-renewal   and B ceor (TCR)  signaline cancer . Interesesponses –64. In aarcinoma . However (mESCs) . Moreove-renewal . As a reFLT3-ITD and N- or K-RAS mutations, confer a proliferative advantage to cells. Class II mutations serve primarily to block hematopoietic differentiation. As a result, aberrations in several canonical pathways associated with cell proliferation and differentiation, such as the STAT5, RAS/MAPK, PI3K/AKT, Notch, and Wnt pathways, have been associated with leukemogenesis (DNTT and FRMD6, ALOX15B is constitutively expressed in human monocyte-derived macrophages. Although the function of ALOX15B in macrophages remains unclear (Current dogma holds a “2-hit” model for leukemogenesis, which suggests that the development of AML is associated with dual dysfunction in cell proliferation and hematopoietic differentiation. Class I mutations, such as ogenesis . Given t unclear , these iIn conclusion, this study identified nine prognosis-related genes in CN-AML and constructed an accurate and robust predictive Cox regression model that is suitable for BMMCs. The predicted risk score could serve as a powerful prognostic indicator, independent of other risk factors. Furthermore, our results shed new light on the pathogenesis of CN-AML and a new potential therapeutic target.The original contributions presented in the study are included in the article/LY collected and analyzed the data and wrote the manuscript. LY and HZ interpreted the results. XY and TL edited the paper and revised the manuscript. SM, HC, KY, and TC revised the manuscript critically. KY and TC performed a final approval of the version to be published. All authors have read and approved the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by grants from the Ministry of Science and Technology of China [2020YFE0203000], the National Natural Science Foundation of China .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


CRIP1 in patients with OC.  With the development of sequencing technology, an increasing number of biomarkers have been identified in ovarian cancer (OC). However, there have been few comprehensive analyses of CRIP1 expression. Kaplan-Meier survival analysis was used to explore the difference in survival in each clinical subgroup. In addition, univariate and multivariate Cox regression analyses were further used to confirm the independent prognostic values of CRIP1. We further constructed ceRNA network based on the difference analysis. Subsequently, we used the ssGSEA algorithm to excavate the correlation between CRIP1 and tumor-infiltrating immune cells. Moreover, the potential biological functions of CRIP1 were investigated by gene function annotation. Finally, we knocked down CRIP1 gene for preliminary biological function verification in A2780 and SKOV-3 cell lines.  Logistic regression analysis was conducted to analyze the correlations between clinical characteristics and CRIP1 was confirmed in The Cancer Genome Atlas (TCGA) cohort, immunohistochemistry, and OC cell lines. CRIP1 overexpression was correlated with the FIGO stage according to a logistic regression analysis that used the median of CRIP1 expression as a categorization of the dependent variable. Survival analysis revealed that CRIP1 was associated with a poor prognosis in most clinical subgroups and acts as an independent prognostic marker in OC patients. In immuno-bioinformatics analysis, CRIP1 is associated to majority of immune cells. This is noteworthy given that we identified that the ceRNA network based on CRIP1 may regulate progression in OC. In addition, gene enrichment analysis suggested CRIP1 may be involved in the JAK-STAT signaling pathway, etc. Finally, we found that knockdown CRIP1 could inhibit the proliferation of OC cells.  The overexpression of CRIP1 is an indicator of poor prognosis and a potential target for immunotherapy in patients with OC. We provided robust evidences that  Globally, ovarian cancer (OC) is an important cause of gynecological cancer-related death. Because a large proportion of patients lack specific clinical manifestations in early stage, resulting in 70% of patients being diagnosed at an advanced stage , therefoCRIP) is one of the important members of LIM/double zinc-finger protein family [CRIP1 is involved in intestinal zinc transport [CRIP1 is overexpressed in immune cells of the epithelium and may play an important role in gut immunity [CRIP1 is overexpressed in all gynecological tumors in pan-cancer analysis. Although one study has discovered the mechanism of CRIP1 in cervical cancer [Cysteine-rich intestinal protein . The expression of CRIP1 protein was excavated from the Human Protein Atlas (HPA) database, including immunohistochemical images and statistical results of staining intensity of different tissues.We download RNA-sequence data (Level-3 HTseq-FPKM) from the Pan-cancer Project in The Cancer Genome Atlas (TCGA) Database. The expression of CRIP1 of OC patients, which is used to select “high-CRIP1” and “low-CRIP1” groups. Kaplan-Meier survival analysis and log-rank test were used to suggest the survival differences  in two groups. In addition, independent prognostic factors were identified by univariate and multivariate Cox regression analyses. In addition, we used ‘rms' package in R software to plot a nomogram for visualizing the prognosis value of CRIP1. The distinction and calibration were evaluated by the ROC curve and calibration curve.The clinical data was downloaded from the TCGA database. The clinical characteristics include FIGO stage, age, BMI, histologic grade, FIGO stage, tumor status, lymphatic invasion, anatomic neoplasm subdivison, and tumor residual; survival information include overall survival (OS), disease-specific survival (DSS), and progress-free interval (PFI), as detailed in CRIP1 and low-CRIP1) based on the median expression of CRIP1. The differentially expressed genes (DEGs) in CRIP1-high samples and CRIP1-low samples were screened using ‘limma' package in R software. The thresholds were set to ∣log2(FC) | >2 and p.adj < 0.05. Moreover, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using related packages. The thresholds were set to p.adj < 0.05 and q value < 0.2.We divided samples into two groups  based on the median expression of CRIP1. The differentially expressed lncRNAs (DELs) in high-CRIP1 samples and low-CRIP1 samples were screened using ‘deseq2' package in R software. The thresholds were set to ∣log2(FC) | >2 and p.adj < 0.05. We used Starbase (http://starbase.sysu.edu.cn/) to predict target miRNAs of CRIP1. Correlation analysis was conducted between all target miRNAs predicted by databases and CRIP1. Finally, we only retained miRNAs that was negatively correlated with CRIP1 and analyzed the prognostic value of the above miRNAs in OC patients. The miRNA (the study was miR-503-5p) involved in the ceRNA network construction were determined by the above methods. At the same time, we also predicted the targeted-lncRNAs of miR-503-5p in the Starbase. Subquently, we constructed the intersection of targeted-lncRNAs and the above DELs as our final lncRNAs involved in the ceRNA network. In addition, we also conducted correlation analysis and survival analysis of the abovementioned lncRNAs. Finally, these genes were combined to construct a lncRNA-miRNA-mRNA CRIP1 network by Cytoscape software.As previously mentioned, we divided samples into two groups . In addition, CRIP1 and GAPDH gene primers were as follows: CRIP1-F: 5′-GGAGCGAGATCCCTCCAAAAT-3′, CRIP1-R: 5′-GGCTGTTGTCATACTTCTCATGG-3′ and GAPDH-F: 5′-AATTCGGCACGAGGCATGATCCAA-3′, GAPDH-R: 5′-AGAAGCCCCAGGAAAAGACTGACA-3′. The details of the methods are provided in references [In this study, we used cell culture, transfection, CCK-8, and qRT-PCR as in vitro assays. The Shanghai Cell Institute Country Cell Bank provided the cell lines SKOV-3, A2780, and IOSE80. GenePharma generated and annealed small-interfering RNA (si-RNA-1/2/3) oligos for rocedure , each sip < 0.05 was considered statistically significant.All statistical analyses were performed using the R software (v.3.6.3). Detailed statistical methods about Transcriptome data are covered in the bioinformatics method section. CRIP1 in various cancers and focused on OC. We found that there were significant differences in the expression of CRIP1 in pan-cancer patients , as shown in Figures CRIP1 was upregulated in all gynecological tumors. In addition, we explored expression of CRIP1 by combining 88 normal ovarian tissue samples in GTEx database, and the same results were found .Correlation analysis was performed between ics: age , histoloics: age , lymphatics: age , venous ics: age , anatomiics: age , and tumics: age . FurtherCRIP, we performed survival analysis of clinical subgroup. Firstly, we calculated the median expression of CRIP1 of OC patients, which is used to select “high-CRIP1” and “low-CRIP1” groups. Kaplan-Meier survival analysis and log-rank test were used to suggest the survival differences  in two groups. The results showed that except for PFS, the survival time of the high-expression group is significantly shorter than the low-expression group  based on the median expression of CRIP1. The DELs and DEGs in two groups were screened using the thresholds set to ∣log2(FC) | >2 and p.adj < 0.05. Finally, we screened out 100 differential genes, of which 88 were downregulated and 12 upregulated . All the three lncRNAs were negatively correlated with miR-503-5p . Finally, these genes were combined to construct a ZNF460-AS1/NORAD/ENTPD1-AS1-miR-503-5p-CRIP1 network  compared to IOSE80, as shown in CRIP1 and si-NC were transfected in A2780 and SKOV-3 cells, respectively, and qRT-PCR and western blot were used to detect the protein expression of CRIP1. It was found that CRIP1 expression was downregulated in OC cell lines with transfection (CRIP1 (To further validate the above bioinformatics results, we detected the expression level of sfection . Similarn (CRIP1 .CRIP1 in OC. Although CRIP1 can promote or inhibit the development of different tumors under different conditions, it has not been discussed in detail in OC.With the rapid development of second-generation sequencing and bioinformatics, it is easier to obtain differentially expressed genes in OC . It provCRIP1 expression and clinical variables was investigated using logistic regression analysis and clinical correlation analysis. We also performed Kaplan-Meier and Cox regression analyses to see if there was an association between CRIP1 expression and survival outcomes. We also analyzed the relationship between CRIP1 expression and immune-infiltrating cells. The possible mechanism of CRIP1 was then investigated using GO and KEGG. Finally, in OC cell lines, the ceRNA regulation network based on CRIP1 was built and confirmed. To our knowledge, no study has been done on CRIP1 expression in OC and its possible predictive significance. CRIP1 has been demonstrated in a few studies to operate as an oncogene. Zhang et al. [CRIP1 in cervical cancer was associated with shorter survival time. Our data also support the carcinogenic effect of CRIP1. On the contrary, a few studies have shown that CRIP1 may have an antitumor effect. Baumhoer et al. [CRIP1 was associated with good prognosis and less metastasis in patients with osteosarcoma.The correlation between g et al.  found thr et al.  found thCRIP1 expression. We further explored the role of CRIP1 in ovarian cancer by qRT-PCR and western blot in the OC cell line. This study may provide a new understanding of the pathogenesis of OC and reveal potential therapeutic targets.In conclusion, based on public sequencing and clinical data, we constructed ceRNA regulatory network based on CRIP1 is an indicator of poor prognosis and a potential target for immunotherapy in patients with OC.We provided robust evidences that